Sales and output multipliers tend to be around the double of income multipliers

Sales and output multipliers tend to be around the

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effect). Sales and output multipliers tend to be around the double of income multipliers (Heng and Low, 1990). 3.1.5.2 Multipliers and features of destination region The value of the multipliers crucially depends on leakages, and therefore on the share of imports on total output. In turn, the share of import is heavily dependent on the size of the region (small economies are relatively less self-contained than larger economy). In the specific case of tourism multipliers, the interrelationships of tourism industries with the rest of the local economy (and specifically the extent to which demand from tourism industries is satisfied with imports), is also a crucial factor. Income multipliers reach a maximum for large countries such as Turkey and the UK (Fletcher, 1989) and in self-contained small island economies (Jamaica, Mauritius), where they vary in the range 0.50-1.20. They are just smaller for US states (range 0.40- 0.90 – Archer, 1988), but sensibly lower in very open regional and urban economies such as US and UK counties (range 0.20-0.50 – Fletcher, 1989; Archer, 1982). Baaijens et al (1998) analyzed statistically (regression models) income multipliers extracted from 11 studies. A positive relationship was found with the logarithm of the population (several alternative regional characteristics - as area size, number of tourist arrivals - were also tested). A similar result was found by Chang (2001), analyzing more than 100 regional IO models varying in size and economic development (covering five US-states: California, Colorado, Florida, Michigan and Massachusetts), generated by means of the IMPLAN IO modelling system. A ‘tourism multiplier’ was defined as a weighted sum of multipliers derived from four tourism related sectors (lodging, eating and drinking, recreation and retail). For all the four analyzed Type II ‘tourism multipliers’ (sales, income, value added and job) the most significant predictor, in a stepwise regression analysis, was found to be the logarithm of population. While sales, income and value added multipliers increased almost linearly with the logarithm of population, the employment multiplier showed a negative correlation (interpreted on the basis that, in the contest of the analyzed dataset, regions characterized by a smaller number of inhabitants tend to correspond to less economic developed rural areas). Using hotels as an example, higher job to sales ratio could be a result of lower room rates, or more part time and seasonal jobs (resulting in lower average wages).
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24 Figure 1: distribution of income (left) and of employment (jobs per million dollars in sales, right) Type II multipliers vs. Log (Population) for 114 US regions. The empty blue diamonds report the results obtained through IO modelling (IMPLAN), while the magenta squares correspond to the corresponding results from a statistical regression analysis, with Log (Population) as dominant predictors. In brown are also reported empirical multipliers proposed from a straightforward classification of the different regions in ‘rural’, ‘small metro’, ‘large metro’ and ‘State’ (Chang, 2001) 3.1.5.3
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